Examples of systems that are capable of estimating a communication bandwidth in a communication network include systems described in PTL 1 to PTL 6.
PTL 1 discloses a network bandwidth measurement system that estimates a communication bandwidth based on a sequence of a plurality of packets with gradually increasing size or gradually decreasing size. For convenience of explanation, a sequence of a plurality of packets with gradually increasing size or gradually decreasing size will be hereinafter referred to as a “packet train”.
The network bandwidth measurement system includes a packet generation unit, a packet transmission unit, a reception interval measuring unit and a bandwidth computing unit. The packet generation unit generates a sequence of a plurality of packets with gradually increasing size or gradually decreasing size. The packet transmission unit transmits the plurality of generated packets at predetermined transmission intervals. The reception interval measuring unit sequentially receives each packet and measures reception intervals each representing an interval between timings at which packets are received. The bandwidth computing unit estimates a communication bandwidth in a communication network on the base of the largest packet size among packets whose reception interval is equal to their transmission interval.
PTL 2 discloses a usable bandwidth measurement system that estimates a communication bandwidth on the base of time needed to transmit and receive packets with increasing size by a fixed common difference. The usable bandwidth measurement system has the function of changing packet size on the base of the estimated communication bandwidth.
PTL 3 discloses a flow rate prediction device that generates a stochastic process model for estimating communication throughput based on the communication throughput of a communication network, for example. The flow rate prediction device determines, based on communication throughput changing over time, whether the communication throughput is in a steady state or non-steady state. The flow rate prediction device then selects a stochastic process model for estimating the communication throughput based on the determination result and computes parameters of the selected stochastic process model.
PTL 4 discloses a parameter estimating device that determines, based on communication throughputs acquired before a first time point, a probability density function for estimating a communication throughput at a second time point.
PTL 5 discloses a degradation avoiding method that identifies, based on transmission/reception qualities of a plurality of media, a medium with degraded quality of transmission/reception processing and determines whether to reduce the rate of communication flow on the medium or not. The degradation avoiding method identifies a medium with degraded quality of transmission/reception processing in accordance with correlation between priorities of a plurality of media and the degradation degree of transmission/reception quality of the media and reduces the rate of communication flow on the media correlated to the identified medium.
PTL 6 discloses a delay variation prediction device, relating to a packet, that identifies an ARCH type model on the base of a delay time difference that changes over time and estimates jitter on the base of the identified ARCH type model. ARCH type modeling is a well-known method for precisely modeling a transition of volatility in the fields of financial engineering and econometrics. The delay variation prediction device estimates changes in jitter as statistically estimated quantities concerning time series representing delay time differences in accordance with the ARCH model. The delay variation prediction device computes parameters of an ARCH type model on the base of delay time differences. ARCH is abbreviation of Autoregressive conditional heteroscedasticity.